A quick demonstration of capping the lines.
First, load the package, generate a dataset and display it.
library(ggplot2)
## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
## Registered S3 method overwritten by 'dplyr':
## method from
## as.data.frame.tbl_df tibble
library(lemon)
dat1 <- data.frame(
gp = factor(rep(letters[1:3], each = 10)),
y = rnorm(30),
cl = sample.int(3, 30, replace=TRUE),
cl2 = sample(c('a','b','c'), 30, replace=TRUE)
)
my.theme <- theme_light()
(
p <- ggplot(dat1, aes(gp, y)) + geom_point() + my.theme
)
NB: In order to manipulate the axis lines, they must be drawn.
Modify the theme so the panel.border
is not drawn (it will be on top of the axis lines),
and have the axis lines drawn:
my.theme <- my.theme + theme(panel.border=element_blank(),
axis.line = element_line(),
axis.ticks = element_line(colour='black'))
p <- p + my.theme
Now, let's have some fun.
We cap the bottom axis line to the right-most tick. The left end is also capped by the amount
specified with the gap
argument (at time of writing, defaulted at 0.01
).
p + coord_capped_cart(bottom='right')
To keep the axis lines consistent, we also specify the left
argument, which still caps the left
axis line by the amount specified with the gap
argument.
p + coord_capped_cart(bottom='right', left='none')
To avoid overplotting, we can apply a jitter. To emphasise that the x-axis is categorical, we can place brackets. We finally polish the plot by removing the redundant vertical grid lines.
ggplot(dat1, aes(gp, y)) + geom_point(position=position_jitter(width=0.2, height=0)) +
coord_capped_cart(left='none', bottom=brackets_horizontal()) +
my.theme + theme(panel.grid.major.x = element_blank())
coord
objectsggplot2's Cartesian coordinates systems, coord_cartesian
, have been extended
to allow for flexible specification of how axes are drawn.
You've seen them above.
The following table summarises the connection between ggplot2's coord functions
and those of lemon.
ggplot2 | lemon's flexible | lemon's short hand |
---|---|---|
coord_cartesian |
coord_flex_cart |
coord_capped_cart |
coord_flip |
coord_flex_flip |
coord_capped_flip |
coord_fixed |
coord_flex_fixed |
– |
The short hand functions in the table's right column simply are almost identical
to those in the middle column.
If one of the side arguments are specified with a character value, the
relevant axis drawing function is used. You can however choose to use e.g.
brackets_horizontal
in place.
The functions capped_horizontal
or brackets_vertical
returns a function
that is called when ggplot2 prints the plot.
In this package, we use ggplot2 to build the axes, then modify in place the
return grobs.
The function is called by the coord
objects when printing the plot,
and is called with the arguments
scale_details, axis, scale, position, theme
The function should then return a grob.
Some pointers to how it is used can be found in ggplot2's help pages on
ggproto (?"ggplot2-ggproto"
)
The arguments are as follows[tip_rds]
[tip_rds]: To get the contents of the arguments, I usually include a line
in the function's code to save them as a RDS object:
saveRDS(list(scale_details=scale_details, axis=axis, scale=scale, position=position, theme=theme), file='whatever.rds')
scale_details
: Details of the scales in 'npc' units (see grid::unit
).
In the example below, a secondary y-axis is used, so we find both
y.major
, etc., and y.sec.major
, etc.
List of 20
$ x.range : num [1:2] 0.4 3.6
$ x.labels : chr [1:3] "a" "b" "c"
$ x.major : num [1:3] 0.187 0.5 0.812
$ x.minor : NULL
$ x.major_source : int [1:3] 1 2 3
$ x.minor_source : NULL
$ x.arrange : chr [1:2] "secondary" "primary"
$ y.range : num [1:2] -2.17 2.26
$ y.labels : chr [1:5] "-2" "-1" "0" "1" ...
$ y.major : num [1:5] 0.0378 0.2635 0.4892 0.7149 0.9406
$ y.minor : num [1:9] 0.0378 0.1506 0.2635 0.3763 0.4892 ...
$ y.major_source : num [1:5] -2 -1 0 1 2
$ y.minor_source : num [1:9] -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
$ y.sec.range : num [1:2] -10.8 11.3
$ y.sec.labels : chr [1:5] "-10" "-5" "0" "5" ...
$ y.sec.major : num [1:5] 0.038 0.263 0.489 0.715 0.941
$ y.sec.minor : num [1:9] 0.0378 0.1506 0.2635 0.3763 0.4892 ...
$ y.sec.major_source: num [1:5] -10 -5 0 5 10
$ y.sec.minor_source: num [1:9] -10 -7.5 -5 -2.5 0 2.5 5 7.5 10
$ y.arrange : chr [1:2] "primary" "secondary"
The secondary y-axis mulitplied the values by 5.
Ticks are drawn at major
coordinates, as are major grid lines.
Minor grid lines, if the theme supports them, are drawn at minor
.
Observe the connection between y.label
and y.major_source
; it only becomes
apparent when also observing the same for the x-axis.
The x.major_source
are the factor integers.
axis
: Character, either "primary"
or "secondary"
. Recall that the
function is called per axis drawn.
scale
: Character, either "x"
or "y"
.
position
: Character, one of the sides, "top"
, "right"
, "bottom"
, or
"left"
.
theme
: The plot's theme. This is however not in absolute terms,
e.g. so text sizes may be described in relative terms to the base size.
To resolve a usable gp
(graphic parameters, see ?grid::gpar
) for a grob,
use ggplot2:::element_render(theme, 'axis.text.x')
, in which the
second argument would resolve to the labels of the x-axis.
The brackets comes in two orientations:
brackets_horizontal
and brackets_vertical
.
If you attempt to use a vertical bracket on a horizontal axis, it will fail with
a undescriptive error.
The bracket functions accept a direction
argument, which can be used to control
which direction the end-points are pointing:
p <- ggplot(mpg, aes(cyl, hwy, colour=class)) +
geom_point(position=position_jitter(width=0.3)) +
scale_x_continuous(breaks=c(4,5,6,8), sec.axis=dup_axis()) +
scale_y_continuous(sec.axis=dup_axis()) +
coord_flex_cart(bottom=brackets_horizontal(),
top=brackets_horizontal(direction='down'),
left=brackets_vertical(),
right=brackets_vertical(direction='right')) +
my.theme
p
The look of the brackets are controlled via theme(axis.ticks)
.
The length of the end-point are controlled via the theme
theme(axis.ticks.length)
. If these needs to be specified for each margin,
use the argument tick.length
:
p <- ggplot(mpg, aes(cyl, hwy, colour=class)) +
geom_point(position=position_jitter(width=0.3)) +
coord_flex_cart(bottom=brackets_horizontal(tick.length=0),
left=brackets_vertical(tick.length = unit(1, 'cm'))) +
my.theme
p
As shown above, using tick.length=unit(0, 'cm')
results in a flat line.
Having produced such wonderous axes, it is a pity they are not plotted around all panels when using faceting.
dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
(d <- ggplot(dsamp, aes(carat, price)) +
geom_point(aes(colour = clarity)) +
coord_capped_cart(bottom='none', left='bottom') +
facet_grid(.~cut) + my.theme
)
facet_grid
and facet_wrap
have been implemented in versions that display
the axis lines (and labels) on all panels. They work exactly like ggplot2's
functions, and are named with _rep_
:
d + facet_rep_grid(.~cut)
If we want the labels shown as well, use the argument:
d + facet_rep_grid(.~cut, repeat.tick.labels = TRUE)
It also works for facet_wrap
:
(d2 <- d + facet_rep_wrap(~cut, ncol=2))
Finally, the legend can be repositioned to fit in a panel by using
reposition_legend
.
See the vignette legend
.
The addition of theme(legend.background)
is merely to provide a border around
the legend.
d2 <- d2 + guides(colour=guide_legend(ncol=3)) +
theme(legend.background = element_rect(colour='grey'))
reposition_legend(d2, position='center', panel='panel-2-3')
As of lemon v0.4.2, you can now use symmetric y- og x-axis scales.
p + scale_y_symmetric(mid=0)
The same effect could be achieved with coord_cartesian
or
use of scale_y_continuous
; however when used with a facet
where each row (or column) should scale to the data,
scale_x_symmetric
and scale_y_symmetric
ensures the data
will remain centered.